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Record W1845660877 · doi:10.3109/02699206.2015.1079247

Effects of blindness on production–perception relationships: Compensation strategies for a lip-tube perturbation of the French [u]

2015· article· en· W1845660877 on OpenAlexafffund
Lucie Ménard, Christine Turgeon, Paméla Trudeau-Fisette, Marie Bellavance-Courtemanche

Bibliographic record

VenueClinical Linguistics & Phonetics · 2015
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsCentre for Research on Brain Language and MusicUniversité du Québec à Montréal
FundersLuonnontieteiden ja Tekniikan Tutkimuksen ToimikuntaNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsPsychologyAuditory feedbackAudiologyVowelSpeech productionPerceptionVisual feedbackBlindnessPhoneticsAuditory perceptionTongueCommunicationSpeech recognitionLinguisticsOptometry

Abstract

fetched live from OpenAlex

The impact of congenital visual deprivation on speech production in adults was examined in an ultrasound study of compensation strategies for lip-tube perturbation. Acoustic and articulatory analyses of the rounded vowel /u/ produced by 12 congenitally blind adult French speakers and 11 sighted adult French speakers were conducted under two conditions: normal and perturbed (with a 25-mm diameter tube inserted between the lips). Vowels were produced with auditory feedback and without auditory feedback (masked noise) to evaluate the extent to which both groups relied on this type of feedback to control speech movements. The acoustic analyses revealed that all participants mainly altered F2 and F0 and, to a lesser extent, F1 in the perturbed condition - only when auditory feedback was available. There were group differences in the articulatory strategies recruited to compensate; while all speakers moved their tongues more backward in the perturbed condition, blind speakers modified tongue-shape parameters to a greater extent than sighted speakers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.194
GPT teacher head0.440
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2015
Admission routes2
Has abstractyes

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